đ§âđ Mission Accomplished: How an Engineer-Astronaut Prepared Metaâs CRAG Benchmark for Launch in Docker
Every ML system is like a spacecraft â powerful, intricate, and temperamental.
The CRAG (Comprehensive RAG Benchmark) from Meta AI is the control panel for Retrieval-Augmented Generation systems.
As is often the case with research projects, CRAG required engineering adaptation to operate reliably in a modern environment:
đ§° I wanted to bring CRAG to a state where it could be launched with a single command â no dependency chaos, no manual fixes.
đ github.com/astronaut27/CRAG_with_Docker
In the original build, several issues made CRAG difficult to run:
đ§ Conflicting library versions;
đŠ An incorrect PYTHONPATH broke the mock-API launch;
âïž No unified, reproducible start-up workflow.
Now, everything comes to life with a single command:
docker-compose up --build
After building, two containeâŠ
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